Artificial Intelligence (AI) in M&A

Session 1 - Introduction and Overview

This section provides M&A professionals with essential context on artificial intelligence and its practical applications in deal-making.

This section of the course is designed to equip M&A professionals with a clear, concise overview of artificial intelligence (AI) and machine learning (ML). Participants will learn key definitions and terminology that demystify what AI systems are, what they can do, and  where their limitations lie.

To achieve this, we provide a practical understanding of core machine learning concepts, as well as deep neural networks and large language models. The course then covers common failure modes of AI systems, mitigation strategies, and ethical and environmental considerations. This will assist participants to work more effectively with modern AI tools, knowing which tasks they are well-suited for, and how to avoid common pitfalls.

• What is AI & Machine Learning?
  • Basic definitions and core concepts
  • Understanding the AI landscape
  • Key types: Supervised, Unsupervised, and Reinforcement Learning

• Large Language Models (LLMs)
  • What are LLMs and how they work
  • Training process and capabilities
  • Current limitations and failure modes

• The "Attribution" Problem
  • Understanding AI hallucinations
  • Why LLMs sometimes generate incorrect information
  • Mitigation strategies for professional use

• Practical Applications
  • Current AI tools in M&A workflows
  • Realistic expectations vs. hype
  • Quality control considerations

•  Risk Management
  • Data security and confidentiality
  • Professional liability considerations
  • Building verification processes

Session 2: Core Prompting Principles

Note these prompting principles apply to a wide range of disciples not only M&A but also law, corporate finance

• Universal Application Across AI Platforms:
  • Prompting principles apply to all major LLMs (ChatGPT, Claude, Gemini, etc.)
  • Techniques are now particularly relevant for Microsoft Copilot (which now has access to GPT-5 & Anthropic /Claude
  • Framework ensures consistency regardless of which AI platform your organisation deploys

• Where is AI being used in M&A
  • Where AI Works Effectively in M&A
  • Where AI Has Limitations in M&A

• Setting Up Your LLM for professional Use
  • Profile Configuration
  • Custom Instructions Examples

• Understanding Data Limitations & Constraints
  • Token Limitations across platforms
  • Various Strategic workarounds
  • Professional Data Considerations

• Practical Verification & Prompting Techniques
  • Hallucination filters
  • Temperature filters

• SCOPE Framework Overview:
  • The SCOPE Framework – systematic approach to prompting
  • Components of the SCOPE framework
    • S = Self: Your role, professional context and tone
    • C = Context: Deal situation & participants
    • O = Objective: What decision this supports
    • P = Parameters: Format, length, detail level, style, tone
    • E = Execute: Precise execution instructions

• Enhanced prompting techniques
  • Chain of Thought methodology – use and application
  • Tree of Thought methodology – use and application
  • Combined CoT & ToT methodology – use and application
  • Sequential prompting

• Agents in M&A
  • Emerging applications
  • Key limitations

Session 3: M&A-Specific Prompting case studies worked examples

This session demonstrates advanced AI prompting techniques through four comprehensive case studies drawn from live M&A transactions. Rather than theoretical exercises, each example walks through the complete methodology progression from initial prompt development to sophisticated output refinement.

Each case study follows a structured demonstration sequence: we begin with the commercial challenge and stakeholder dynamics, then observe the systematic application of the SCOPE framework to develop targeted initial prompts. The core demonstration involves live Chain-of-Thought and Tree-of-Thought methodologies, showing how sequential prompt refinement transforms basic outputs into genuinely useful professional work product.

Case Studies

Case Study 1: Valuation (Trading Comparables Analysis)

Case Snapshot: Corporate finance director at investment bank advising on SaaS business valuation requiring comprehensive trading comparables analysis to establish credible valuation range for a £500M technology acquisition. Client needs systematic approach to identify truly comparable companies, filter out inappropriate matches, and derive defensible multiple ranges that will withstand buyer scrutiny during negotiations.

AI Methodology Walkthrough: Using SCOPE framework to develop initial prompts, then step-by-step demonstration of how Chain-of-Thought methodology systematically identifies relevant peer companies, filters based on business model similarities (recurring revenue, growth rates, geographic focus), and calculates meaningful multiple ranges. Sequential prompting refinement shows progression from basic comparable identification to sophisticated analysis incorporating size adjustments, growth differentials, and market positioning factors that enable credible valuation positioning.

Case study 2. Negotiation Strategy Development

Case Snapshot: Investment banker advising on acquisition of German automotive supplier requiring sophisticated deal positioning strategy that accounts for multiple bidder dynamics, synergy assumptions, and stakeholder priorities. Client needs tactical negotiation framework that can adapt to changing bid dynamics whilst maintaining credible pricing rationale and preserving negotiating leverage throughout the auction process.

AI Methodology Walkthrough: Using SCOPE framework to develop initial prompts, then step-by-step demonstration of how Chain-of-Thought methodology systematically analyses seller motivations (CEO seeking maximum value, CTO prioritising cultural fit, investors wanting certainty), develops tiered pricing strategies with supporting rationale, and applies Tree-of-Thought planning to anticipate competitor responses and maintain tactical flexibility when uncertainties arose regarding projected NPV savings from operational synergies, forcing comprehensive re-evaluation of bid positioning and price justification during critical mid-auction phase.

Case study 3: Earn-outs (Earn-Out Structure Optimisation for Multi-Seller Scenarios)

Case Snapshot: Senior M&A lawyer advising MediaGroup PLC (listed strategic acquirer) on competitive auction requiring differentiated earn-out proposal targeting 3 founding partners with different exit preferences and risk appetites. Client requires sophisticated earn-out structure differentiating their offer from standard approaches in hotly contested process.

AI Methodology Walkthrough: Using SCOPE framework to develop initial prompts, then step-by-step demonstration of how Chain-of-Thought methodology systematically analyses individual seller preferences (CEO wants maximum upside, CTO seeks balance, investor wants certainty) followed by Tree-of-Thought planning that enables dynamic structure adjustment when rival bidders modify their offers mid-auction.

Case study 4. Locked Box – leakage analysis

Case Snapshot: Senior M&A lawyer advising TechCorp Ltd (strategic acquirer) on locked box mechanism for £250m acquisition of family-owned manufacturing business requiring comprehensive leakage framework during 8-month completion timeline. Complex intercompany trading arrangements with retained family companies (£25m annual sales, £15m property rental) create sophisticated value transfer risks requiring careful classification and monitoring.

AI Methodology Walkthrough: Using SCOPE framework to develop initial prompts, then step-by-step demonstration of how Chain-of-Thought methodology systematically classifies prohibited vs conditional vs notification leakage categories (family dividends, intercompany pricing, transfer arrangements) followed by Tree-of-Thought planning that enables dynamic response to pricing disputes and volume changes during extended period.

Session 1 of this AI in M&A course is led by a trainer pursuing a PhD in Machine Learning at the Gatsby Unit of University College London (UCL). His research primarily revolves around developing novel machine learning algorithms with greater efficiency and robustness guarantees. His work has been previously published and selected for presentation at the International Conference on Artificial Intelligence and Statistics (AISTATS). The Gatsby Unit has produced ground-breaking AI research since its inception in 1998. Its alumni include two 2024 Nobel Prize winners in Physics and Chemistry and the co-founders of DeepMind - one of the world's leading AI research labs.

This trainer also has over 5 years of commercial experience applying advanced AI techniques during his previous role as an econometrician/data scientist at PwC. His previous projects included:

• Using machine learning to estimate the growth impact of SME lending for a leading retail bank
• Developing PwC’s AI Nowcasting Model for accurate UK GDP predictions
• Causal machine learning to estimate the effectiveness of marketing activities for a global airline
• Econometric methods to analyse the effect of CEO contract incentives on share buybacks for the UK Department of Business, Energy and Industrial Strategy.

Academically, he has achieved distinctions in two master's degrees from UCL - an MSc in Computational Statistics and Machine Learning in 2021 (Dean’s List) and an MSc in Economics in 2016.

Sessions 2 and 3 are led by a consultant, public speaker, and author with over 45 years of experience in private equity, debt advisory, restructuring, and infrastructure. He is a Senior Advisor to KPMG Finland and a Senior Consultant to Grant Thornton UK.

The consultant provides training programmes to a wide range of blue-chip clients in Europe, Africa, the Middle and Far East, North America, Asia-Pacific and China. In-house clients include banks (BNP Paribas, Société Générale, ING, Barclays Capital, Bank of China, RBS, SEB); lawyers (Kirkland and Ellis, Baker & McKenzie, Skadden Arps, Sullivan & Cromwell, Cadwalader, Latham & Watkins, Weil, White & Case); advisory firms (Lazard, PWC, M&A International, KPMG, EY USA, Deloitte); PE firms (Cinven, Advent, Barings Asia, Waterland, AVCAL); corporates (Siemens, Airbus, Turkcell, Candy Crush, Diageo, Statkraft) and governmental bodies (the UKLA, the EBRD, the EIB, the ECGD, Omani Oil Corp.)

He qualified in South Africa both as a Chartered Accountant - with Deloitte and as a lawyer with Hofmeyr - where he was involved in structuring several high-profile project financings including BMW 3 Series, Ford Sierra and GM.

After moving to London, he built an extensive career in corporate finance, serving as a corporate finance executive at Lazard Brothers, an assistant director at Hoare Govett advising listed companies and later joining ABN Amro's cross-border M&A team before becoming a Director in Cross-Border M&A at MeesPierson Corporate Finance. Separately, he has served as a member of the EU-PHARE programme and advised the Estonian government on its privatisation programme.

For 18 years he served as the Programme Director at the City Business School, London, for Infrastructure Finance for the M.Sc. programme in Business Administration and Finance. He has since stepped back from this role to focus on select advisory and consulting engagements.

He also served for approximately 10 years as an advisor to DebtXplain (subsequently acquired by Reorg and now Octus), bringing his extensive knowledge in debt markets and financial restructuring to the organisation before recently transitioning away from this role.

He is a fellow of the Institute of Chartered Accountants in England & Wales and the South African Institute of Chartered Accountants.

Upon completion of this AI in Mergers and Acquisitions course, participants will be able to:

• Develop sophisticated prompt engineering techniques tailored for M&A applications, ensuring consistent and reliable AI outputs.
• Evaluate and select the best AI tools for M&A workflows, understanding their capabilities, limitations, and optimal use cases.
• Create robust quality control frameworks for AI-generated outputs in high-stakes transaction environments.
• Implement effective risk management protocols of AI for M&A due diligence, contract review, and financial analysis.
• Structure and execute artificial intelligence for M&A due diligence that maintains accuracy while significantly improving efficiency.
• Develop strategies for managing AI limitations and biases in M&A contexts, ensuring reliable and trustworthy outputs.

• Investment Banking Professionals:
  • M&A associates and directors seeking to enhance deal execution efficiency
  • Financial modelling specialists looking to integrate AI tools
  • Due diligence teams aiming to automate routine analysis
  • Deal sourcing professionals interested in AI-powered screening tools

• Private Equity and Venture Capital Professionals:
  • Deal teams seeking to streamline transaction processes
  • Portfolio operations managers implementing AI solutions
  • Investment analysts focusing on tech-enabled deal evaluation
  • Due diligence specialists looking to enhance their toolkit

• Legal Professionals:
  • M&A lawyers wanting to leverage AI for contract review
  • Corporate lawyers handling transaction documentation
  • Legal technology officers implementing AI solutions

• Corporate Development Executives:
  • M&A strategy leaders at corporations
  • Corporate development teams that manage deal pipelines
  • Integration specialists handling post-merger processes

• Financial Advisory Professionals:
  • Transaction advisory teams at professional services firms
  • Valuation specialists incorporating AI modelling
  • Due diligence professionals seeking efficiency gains
  • Deal consultants advising on modern M&A practices

• Risk and Compliance Professionals:
  • Deal compliance officers managing AI implementation
  • Risk management specialists in M&A contexts

• Deal sourcing professionals using PitchBook, CapIQ, and AI-powered screening tools
• Due diligence teams working with Kira Systems, Luminance, and document review platforms 
• VDR managers using Datasite, Intralinks, and AI-enhanced data rooms

This comprehensive program equips M&A professionals with cutting-edge AI implementation strategies and practical skills for modern deal-making. Beyond significant time savings, the course emphasises how AI fundamentally enhances the quality of outputs—delivering more precise drafting, better risk identification & tailored strategic insights that drive superior client outcomes.

Stand‑Alone AI Courses for Adjacent Practice Areas

In addition to the AI in M&A programme, we offer separate half‑day and full‑day classes that apply the same prompt‑engineering methodology to other high‑value legal and advisory workflows. Popular courses include:

Course	Primary Audience	Key Skills & Outcomes
AI in Litigation & Dispute Resolution	Litigation teams, arbitration specialists	Draft pleadings, discovery requests and witness outlines; privilege‑preserving document review; precedent search automation.
AI in Restructuring & Insolvency	Restructuring lawyers, turnaround advisors, special‑situations bankers	Rapid covenant‑breach analysis; AI‑driven scenario modelling; stakeholder communications drafting.
AI in Tax Structuring	Transaction‑tax partners, tax analysts	Cross‑border structuring prompt frameworks; anti‑avoidance diagnostic prompts; drafting ruling requests.
AI for Regulatory & Compliance	In‑house counsel, compliance officers	Horizon scanning of emerging regulations; automated risk‑register drafting; regulatory submission generation.
AI‑Enabled ESG Due Diligence	ESG specialists, deal teams	Sustainability clause review; supply‑chain risk flagging; greenwashing detection prompts.

Frequently Asked Questions

Q: We already use Microsoft Copilot. Do we still need this training?
A: Absolutely. Copilot recently acquired access to ChatGPT, making it significantly more powerful than earlier versions—but also making proper prompting technique essential. Most users get mediocre results from Co-Pilot because they treat it like a search engine rather than applying structured prompting frameworks. This course teaches you how to extract genuinely useful M&A analysis from Copilot and other AI tools, rather than generic summaries.

Q: My firm uses DealCloud/PitchBook/Kira Systems. Will this course help with those platforms? 
A: Yes. While we don't provide platform-specific training on proprietary databases (which typically comes from the vendors themselves), we teach the fundamental AI interaction skills that improve your effectiveness on any AI-enabled platform.

Q: What's the difference between this course and vendor training? 
A: Vendor training teaches you how to use their platform's features. This course teaches you how to get better results from AI components regardless of the platform, including prompt engineering, output verification, and risk management skills that transfer across all AI tools.

Q: Will we be doing hands-on prompting exercises during the course?
A: No. The course uses live demonstrations of prompting techniques rather than participant exercises. Developing a sophisticated M&A prompt—particularly using Chain-of-Thought and Tree-of-Thought methodologies—can easily take 30-45 minutes to refine properly. Additionally, the same prompt can generate different results across participants (due to how LLMs work), and some platforms have significant response lag times, which would make synchronised group exercises impractical in a four-hour session.

Instead, you'll observe best-practice prompting in real time through detailed case studies, understanding the complete thought process behind effective AI interaction. You'll also receive prompt templates that you can adapt and use immediately in your own M&A work. This approach gives you both the conceptual framework and practical tools to commission and evaluate AI work effectively—far more valuable than struggling with basic prompts under time pressure.